Spotting Method for Classification of Real World Data

This paper makes the case for a spotting computation scheme which gives rise to a new classification methodology for processing real world data by surveying algorithms developed under the Real World Computing (RWC) program and related work in Japan. A spotting function has the segmentation-free characteristic which ignores gracefully most real world input data which do not belong to a task domain. Some members of the family of spotting methods have been developed under the RWC program. This paper shows how some spotting methods rise to the challenge of the case made for them. The common computational structure amongst spotting methods suggests an architecture for spotting computation.